How to build good AI solutions when data is scarce : data-efficient AI techniques are emerging, and that means you don't always need large volumes of labeled data to train AI systems based on neural networks /
Developing AI systems based on neural networks can require large volumes of labeled training data, which can be hard to obtain in some settings. New techniques for reducing the number of labeled examples needed to build accurate models are now emerging to address this problem. These approaches encom...
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| Format: | eBook |
| Language: | English |
| Published: |
[Cambridge, Massachusetts] :
MIT Sloan Management Review,
2022.
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| Edition: | [First edition]. |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Summary: | Developing AI systems based on neural networks can require large volumes of labeled training data, which can be hard to obtain in some settings. New techniques for reducing the number of labeled examples needed to build accurate models are now emerging to address this problem. These approaches encompass ways to transfer models across related problems and to pretrain models with unlabeled data. They also include emerging best practices around data-centric artificial intelligence. |
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| Item Description: | "Reprint 64202." |
| Physical Description: | 1 online resource (11 pages) : illustrations |
| Bibliography: | Includes bibliographical references. |